• Title, Summary, Keyword: pattern classification

Search Result 1,772, Processing Time 0.037 seconds

A Study on Gender Classification Based on Diagonal Local Binary Patterns (대각선형 지역적 이진패턴을 이용한 성별 분류 방법에 대한 연구)

  • Choi, Young-Kyu;Lee, Young-Moo
    • Journal of the Semiconductor & Display Technology
    • /
    • v.8 no.3
    • /
    • pp.39-44
    • /
    • 2009
  • Local Binary Pattern (LBP) is becoming a popular tool for various machine vision applications such as face recognition, classification and background subtraction. In this paper, we propose a new extension of LBP, called the Diagonal LBP (DLBP), to handle the image-based gender classification problem arise in interactive display systems. Instead of comparing neighbor pixels with the center pixel, DLBP generates codes by comparing a neighbor pixel with the diagonal pixel (the neighbor pixel in the opposite side). It can reduce by half the code length of LBP and consequently, can improve the computation complexity. The Support Vector Machine is utilized as the gender classifier, and the texture profile based on DLBP is adopted as the feature vector. Experimental results revealed that our approach based on the diagonal LPB is very efficient and can be utilized in various real-time pattern classification applications.

  • PDF

Construction of A Nonlinear Classification Algorithm Using Quadratic Functions (2차 하수를 이용한 비 선형 패턴인식 알고리즘 구축)

  • 김락상
    • Journal of the Korean Operations Research and Management Science Society
    • /
    • v.25 no.4
    • /
    • pp.55-65
    • /
    • 2000
  • This paper presents a linear programming based algorithm for pattern classification. Pattern classification is being considered to be critical in the area of artificial intelligence and business applications. Previous methods employing linear programming have been aimed at two-group discrimination with one or more linear discriminant functions. Therefore, there are some limitations in applying available linear programming formulations directly to general multi-class classification problems. The algorithm proposed in this manuscript is based on quadratic or polynomial discriminant functions, which allow more flexibility in covering the class regions in the N-dimensional space. The proposed algorithm is compared with other competitive methods of pattern classification in experimental results and is shown to be competitive enough for a general purpose classifier.

  • PDF

Texture Classification Using Rotation Invariant Local Directional Pattern (Rotation Invariant Local Directional Pattern을 이용한 텍스처 분류 방법)

  • Lee, Tae Hwan;Chae, Ok Sam
    • Convergence Security Journal
    • /
    • v.17 no.3
    • /
    • pp.21-29
    • /
    • 2017
  • Accurate encoding of local patterns is a very important factor in texture classification. However, LBP based methods w idely studied have fundamental problems that are vulnerable to noise. Recently, LDP method using edge response and dire ction information was proposed in facial expression recognition. LDP is more robust to noise than LBP and can accommod ate more information in it's pattern code, but it has drawbacks that it is sensitive to rotation transforms that are critical to texture classification. In this paper, we propose a new local pattern coding method called Rotation Invariant Local Direc tional Pattern, which combines rotation-invariant transform to LDP. To prove the texture classification performance of the proposed method in this paper, texture classification was performed on the widely used UIUC and CUReT datasets. As a result, the proposed RILDP method showed better performance than the existing methods.

Approximate Pattern Classification with Rough set (Rough 집합을 이용한 근사 패턴 분류)

  • 최성혜;정환묵
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • /
    • pp.248-251
    • /
    • 1997
  • In this paper, We propose the concept of approximate Classification in the field of two group discriminan analysis. In our approach, an attribute space is divided into three subspaces. Two subspaces are for given two group and one subspace is for a boundary area between the two groups. We propose Approximate Pattern Classification with Rough set. We also propose learning procedures of neural networks for approximate classification. We propose two weighting methods which lead to possibility analysis and necessity analysis. We illustrate the proposed methods by numerical examples.

  • PDF

Classification of Welding Defects in Austenitic Stainless Steel by Neural Pattern Recognition of Ultrasonic Signal (초음파신호의 신경망 형상인식법을 이용한 오스테나이트 스테인레스강의 용접부결함 분류에 관한 연구)

  • Lee, Gang-Yong;Kim, Jun-Seop
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.20 no.4
    • /
    • pp.1309-1319
    • /
    • 1996
  • The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

Customer Load Pattern Analysis using Clustering Techniques (클러스터링 기법을 이용한 수용가별 전력 데이터 패턴 분석)

  • Ryu, Seunghyoung;Kim, Hongseok;Oh, Doeun;No, Jaekoo
    • KEPCO Journal on Electric Power and Energy
    • /
    • v.2 no.1
    • /
    • pp.61-69
    • /
    • 2016
  • Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

Brain activation pattern and functional connectivity network during classification on the living organisms

  • Byeon, Jung-Ho;Lee, Jun-Ki;Kwon, Yong-Ju
    • Journal of The Korean Association For Science Education
    • /
    • v.29 no.7
    • /
    • pp.751-758
    • /
    • 2009
  • The purpose of this study was to investigate brain activation pattern and functional connectivity network during classification on the biological phenomena. Twenty six right-handed healthy science teachers volunteered to be in the present study. To investigate participants' brain activities during the tasks, 3.0T fMRI system with the block experimental-design was used to measure BOLD signals of their brain. According to the analyzed data, superior, middle and inferior frontal gyrus, superior and inferior parietal lobule, fusiform gyrus, lingual gyrus, and bilateral cerebellum were significantly activated during participants' carrying-out classification. The network model was consisting of six nodes (ROIs) and its fourteen connections. These results suggested the notion that the activation and connections of these regions mean that classification is consist of two sub-network systems (top-down and bottom-up related) and it functioning reciprocally. These results enable the examination of the scientific classification process from the cognitive neuroscience perspective, and may be used as basic materials for developing a teaching-learning program for scientific classification such as brain-based science education curriculum in the science classrooms.

A Study on the Digital Signal Processing for the Pattern fiecognition of Weld Flaws (용접결함의 패턴인식을 위한 디지털 신호처리에 관한 연구)

  • 김재열;송찬일;김병현
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • /
    • pp.393-396
    • /
    • 1995
  • In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

  • PDF

Design of Precipitation/non-precipitation Pattern Classification System based on Neuro-fuzzy Algorithm using Meteorological Radar Data : Instance Classifier and Echo Classifier (기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 강수/비강수 패턴분류 시스템 설계 : 사례 분류기 및 에코 분류기)

  • Ko, Jun-Hyun;Kim, Hyun-Ki;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.64 no.7
    • /
    • pp.1114-1124
    • /
    • 2015
  • In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

Classficiation of Bupleuri Radix according to Geographical Origins using Near Infrared Spectroscopy (NIRS) Combined with Supervised Pattern Recognition

  • Lee, Dong Young;Kang, Kyo Bin;Kim, Jina;Kim, Hyo Jin;Sung, Sang Hyun
    • Natural Product Sciences
    • /
    • v.24 no.3
    • /
    • pp.164-170
    • /
    • 2018
  • Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.